#JOIN anxie_dep_medication & anxie_dep_famfriends using inner_join()sol2020_anxie_dep <- anxie_dep_medication %>%inner_join(anxie_dep_famfriends, by =c("Entity", "Year")) %>%rename(country = Entity, code = Code.x, prescribed_med =`Share - Question: mh8d - Took prescribed medication when anxious/depressed - Answer: Yes - Gender: all - Age group: all`, friends_family =`Share - Question: mh8c - Talked to friends or family when anxious/depressed - Answer: Yes - Gender: all - Age group: all`) %>%select(country, code, prescribed_med, friends_family) %>%drop_na(code)# EDIT gdp_raw using pivot_longer()gdp <- gdp_raw %>%select(!...69&!"Indicator Code"&!"Indicator Name") %>%pivot_longer(names_to ="Year", values_to ="gdp_per_cap", cols =-c(`Country Name`, `Country Code`))#JOIN gdp & suicide_rate_by_country using inner_join()suicide_gdp <- suicide_rate_by_country %>%mutate(Year =as.character(Year)) %>%inner_join(gdp, by =c("Code"="Country Code", "Year")) %>%rename(country = Entity,year = Year,code = Code,suiciderate_per_10k =`Age-standardized death rate from self-harm among both sexes`) %>%select(!"Country Name")## deathrate_per_10k = estimated annual number of suicides per 100,000 people ## gdp_per_cap = GDP per capita in $USD#ADD 'continent' column to suicide_gdp using left_join()suicide_gdp <- country_by_continent %>%left_join(suicide_gdp, by =c("Country"="country")) %>%clean_names()
Introduction
The primary goal of this project is to explore the relationship between GDP per capita and suicide rates across countries. In recent years, the rising prevalence of psychological disorders that impact individual physical and social functioning has drawn increased global attention to mental health as a fundamental aspect of public well-being. In addition, given the well-established link between mental illness (e.g., depression, anxiety disorders, eating disorders, trauma-related disorder) and suicide, this study also investigates two potential protective factors that mitigate the risk of two major ones – i.e., anxiety and depression: (1) access to prescribed medication and (2) interpersonal support from friends and family.
According to the World Health Organization’s fact sheet, 309 million people worldwide were living with some form of anxiety disorders and about 280 million were suffering from depression in 2019 (World Health Organization, Mental Disorders, 2022). An analysis by the Substance Abuse and Mental Health Services Administration (SAMHSA, 2016), which examined the implications of diagnostic shifts from DSM-IV to DSM-V for the National Survey on Drug Use and Health, characterizes anxiety disorders as involving excessive fear and behavioral disturbances. Anxiety disorder exhibits symptoms that persist more days than not. These symptoms must also last over a period of at least six months and negatively impact occupational and/or academic performance (SAMHSA, 2016):
Restlessness
Being easily fatigued
Difficulty concentrating or mind going blank
Irritability
Muscle tension
Sleep disturbance (difficulty falling or staying asleep, or restless unsatisfying sleep)
Furthermore, per SAMHSA (2016), a diagnosis of major depressive disorder requires the presence of either a depressed mood or a loss of interest or pleasure, in addition to at least four of the following symptoms, which are prolonged for a period of at least two consecutive weeks:
Significant changes in appetite or significant unintentional weight loss/gain (more than 5 percent in a month)
Sleep disturbances (insomnia or hypersomnia)
Observable psychomotor agitation and/ or lack of motivaiton
Fatigue or low energy
Feelings of worthlessness or excessive guilt
Difficulty thinking, concentrating, or making decisions
Recurrent thoughts of death, suicidal ideation, or suicide attempts
Rather than testing a predetermined directional relationship, this project sought to investigate whether an association exists between GDP per capita and suicide rates across countries, and to identify patterns in potential protective factors – namely, access to prescribed medication and interpersonal support. Therefore, no specific hypotheses were proposed for this project.
The datasets used in this analysis were compiled from three major sources: the World Bank, the World Health Organization (WHO), and the Wellcome Global Monitor, which was processed by Our World in Data. Additionally, it is worth noting that the most recent data available are from 2021; as such, we acknowledge that the trends identified in this study may not fully represent present-day conditions.
Results
Code
# data frame created for Figure 2suicide_gdp_diff <- suicide_gdp %>%filter(year ==2000| year ==2021) %>%pivot_wider(names_from = year, values_from =c(gdp_per_cap, suiciderate_per_10k), names_prefix ="in_") %>%mutate(gdp_diff = gdp_per_cap_in_2021 - gdp_per_cap_in_2000,suicide_diff = suiciderate_per_10k_in_2021 - suiciderate_per_10k_in_2000,inc_level_2021 =case_when(gdp_per_cap_in_2021 <1045~"Lower", gdp_per_cap_in_2021 >=1045& gdp_per_cap_in_2021 <=4095~"Lower Middle", gdp_per_cap_in_2021 >4095& gdp_per_cap_in_2021 <=12695~"Upper Middle", gdp_per_cap_in_2021 >12695~"Higher")) # inc_level_2021 is defined by World Bank's country classifications by income level in 2021-22suicide_gdp_diff <- suicide_gdp_diff %>%filter(!is.na(inc_level_2021)) %>%mutate(inc_level_2021 =fct_relevel(inc_level_2021, "Higher", "Upper Middle", "Lower Middle", "Lower"))neg_outlier1 <- suicide_gdp_diff %>%select(country, suicide_diff, gdp_diff) %>%filter(suicide_diff ==min(suicide_diff)) %>%mutate(suicide_diff =round(suicide_diff, 1),gdp_diff =format(round(gdp_diff, 1), scientific =FALSE, big.mark =",")) # format() was suggested by ChatGPT to disable the scientific notation presented in html.neg_outlier2 <- suicide_gdp_diff %>%select(country, suicide_diff, gdp_diff) %>%filter(gdp_diff ==max(gdp_diff)) %>%mutate(suicide_diff =round(suicide_diff, 1),gdp_diff =format(round(gdp_diff, 1), scientific =FALSE, big.mark =","))pos_outlier3 <- suicide_gdp_diff %>%select(country, suicide_diff, gdp_diff) %>%filter(suicide_diff ==max(suicide_diff)) %>%mutate(suicide_diff =round(suicide_diff, 1),gdp_diff =format(round(gdp_diff, 1), scientific =FALSE, big.mark =","))
We found no significant correlation between GDP per capita and suicide rates (Figure 1). There was only a non-significant negative correlation between higher levels of economic development and improvements in mental health outcomes worldwide (r = -0.113) (Figure 2; Table 1). Our statistical analysis also suggested that such a pattern persist across countries with different income levels (Table 1). Notably, upper-middle-income countries show the strongest negative correlation (r = -0.523), which suggests that in these nations, increases in GDP were more substantially associated with reductions in suicide rates. In contrast, the impact of GDP growth on suicide reduction is less evident at the extremes of the income spectrum – both high-income (r = -0.028) and low-income countries (r = -0.121) exhibit relatively weak associations. For instance, upper-middle Russia showed a significant decrease in national suicide rates (-29.9 in suicide decrease (per 10,000)) but a very minimal improvement in economic outlook (10,749.9 in GDP increase (in USD)). Conversely, higher-income-level 142,034.3 only exhibited a slight decline in suicide rates over the 21-year period despite its burgeoning national GDP (-2.4 in suicide decrease (per 10,000) and 142,034.3 in GDP increase (in USD)).
Code
# Figure of GDP x Suicide Ratefig_1 <-ggplot(suicide_gdp, aes(x = gdp_per_cap, y = suiciderate_per_10k, color = continent)) +geom_point(size =2, alpha =0.75, show.legend =FALSE) +# 'show.legend' was searched up online (website: https://stackoverflow.com/questions/35618260/remove-legend-ggplot-2-2)scale_x_log10(labels =label_comma()) +# scale_color_manual(values = c("#64cdcc", "#b05aa5", "#f28e2b", "#b10318", "#9d795d","#1777aa")) +facet_wrap(~fct_relevel(continent,"Africa","Asia","Europe","North America","South America","Oceania")) +labs(subtitle ="Year: {frame_time}",x ="GDP per capita (in $USD)", y ="Suicide rate (per 10,000 people)") +theme_linedraw() +transition_time(as.integer(year)) +ease_aes('linear') animate(fig_1, renderer =gifski_renderer()) # this is a gifanim_save("fig_1.gif")
Figure 1: Correlation between GDP per Capita and Suicidal Rate by Continent from 2000 to 2021
Code
# Figure of GDP x Suicide Rate with Line of Best Fitfig_2 <-ggplot(suicide_gdp_diff, aes(x = gdp_diff, y = suicide_diff, color = inc_level_2021)) +geom_point(size =2, alpha =0.75) +geom_smooth(aes(group =1), method ="lm", color ="#ad081f", se =FALSE) +scale_color_manual(values =c("Lower"="#88ac3b","Lower Middle"="#75C78f","Upper Middle"="#0D675F","Higher"="#08200C")) +scale_x_continuous(labels =label_comma()) +labs (x ="GDP per capita (in $USD)", y ="Suicide rate (per 10,000 people)",color ="Income Level") +theme_linedraw() fig_2
Figure 2: Global Correlation between Changes in GDP per Capita and Suicidal Rate by Income Level
Code
# Correlational Tablecorr <- suicide_gdp_diff %>%select(suicide_diff, gdp_diff, inc_level_2021)correlation_table <- corr %>%mutate(inc_level_2021 =as.character(inc_level_2021)) %>%bind_rows(mutate(., inc_level_2021 ="Global")) %>%# bind_row() was suggested by ChatGPT to combine data frames into one single data framegroup_by(Group = inc_level_2021) %>%summarize(Correlation =round(cor(suicide_diff, gdp_diff), 3))kable(correlation_table, caption ="Table 1. Correlation between Change in GDP and Suicide Rate by Income Level")
Table 1. Correlation between Change in GDP and Suicide Rate by Income Level
Group
Correlation
Global
-0.113
Higher
-0.028
Lower
-0.121
Lower Middle
-0.301
Upper Middle
-0.523
Despite the overall trend, a subset of countries exhibited an opposite pattern – a positive correlation between GDP per capita and suicide rates. Again, income level did not show to play a prominent role in mediating the relationship. In other words, certain countries continued to exhibit simultaneous rises in economic output and suicide rate, irrespective of their economic categorization. Some lower middle income countries had a significantly high suicide rate while their GDP per capita experienced a minimal increase over the course of twenty years (e.g., Lesotho with 19.7 increase in suicide rate and 623.8 increase in GDP). Similar positive relationship were also observed in several upper-middle and high-income countries, including Korea and the United States, which may suggest that national wealth alone is not a sufficient predictor of improved population mental health outcomes (Table 2).
table_pos <- table %>%filter(gdp_positive ==TRUE& suicide_positive ==TRUE) %>%select(country, continent, suicide_rate_diff, gpd_diff, gdp_per_cap_in_2021, inc_level_2021) %>%arrange(desc(suicide_rate_diff)) %>%slice(1:10) %>%rename(Country = country,Continent = continent,"GDP in 2021 (in USD)"= gdp_per_cap_in_2021,"Income Level in 2021"= inc_level_2021,"GDP Increase between 2000 and 2021 (in USD))"= gpd_diff,"Suicide Rate Increase between 2000 and 2021 (per 10,000 people)"= suicide_rate_diff) kable(table_pos, caption ="Table 2. Top 10 Countries Ranked by Differences in Suicide Rate between 2000 and 2021 -- Positive Correlation")
Table 2. Top 10 Countries Ranked by Differences in Suicide Rate between 2000 and 2021 – Positive Correlation
Country
Continent
Suicide Rate Increase between 2000 and 2021 (per 10,000 people)
GDP Increase between 2000 and 2021 (in USD))
GDP in 2021 (in USD)
Income Level in 2021
Lesotho
Africa
19.7
$624
$1,067
lower middle
Uruguay
South America
7.4
$10,900
$17,888
higher
Zimbabwe
Africa
7.2
$1,162
$1,724
lower middle
Mozambique
Africa
7.0
$183
$510
lower
South Korea
Asia
6.1
$22,868
$35,126
higher
South Africa
Africa
5.8
$3,626
$6,843
upper middle
Thailand
Asia
4.4
$5,052
$7,058
upper middle
United States
North America
4.1
$34,988
$71,318
higher
Mexico
North America
2.8
$2,790
$10,314
upper middle
Paraguay
South America
2.6
$4,241
$5,977
upper middle
Code
# Load world map with country boundaries -- chatGPT suggest me to use this command:world_map <-ne_countries(scale ="medium", returnclass ="sf")# Check for mismatched namescheck <- sol2020_anxie_dep %>%anti_join(world_map, by =c("country"="name")) %>%select(country)# Renaming countries that don't match world_map$namesol2020_anxie_dep <- sol2020_anxie_dep %>%filter(country !="World") %>%mutate(country =case_when( country =="Bosnia and Herzegovina"~"Bosnia and Herz.", country =="Cote d'Ivoire"~"Ivory Coast", country =="Dominican Republic"~"Dominican Rep.", country =="United States"~"United States of America",TRUE~ country ))# Data for Medicinemed <- world_map %>%left_join(sol2020_anxie_dep, by =c("name"="country"))data_med <- med %>%select(prescribed_med, name) %>%drop_na(prescribed_med)mean_med <- data_med %>%summarise(mean_medval =mean(prescribed_med)) %>%pull(mean_medval)highest_med <- data_med %>%arrange(desc(prescribed_med)) %>%slice(1) %>%select(name, prescribed_med)lowest_med <- data_med %>%arrange(prescribed_med) %>%slice(1) %>%select(name, prescribed_med)# Data for Friends&Family fam <- world_map %>%left_join(sol2020_anxie_dep, by =c("name"="country"))data_fam <- fam %>%select(friends_family, name) %>%drop_na(friends_family)mean_fam <- data_fam %>%summarise(mean_famval =mean(friends_family)) %>%pull(mean_famval)highest_fam <- data_fam %>%arrange(desc(friends_family)) %>%slice(1) %>%select(name, friends_family)lowest_fam <- data_fam %>%arrange(friends_family) %>%slice(1) %>%select(name, friends_family)
Additionally, Figure 3 shows a global distribution of individuals who used prescribed medication for psychological issues in 2020. Higher percentages indicate a greater reliance on pharmacological treatment as part of a their psychological treatment plan; countries with missing data are shown in gray. The average percentage of population use prescribed medication for their anxiety and/or depression is 48.9%. Among all countries, United Kingdom reports the highest usage rate at 76.7%, while Egypt has the lowest at 16%.
Code
# Map of Medicinemap_med <- med %>%ggplot(aes(fill = prescribed_med, line ="black")) +geom_sf() +scale_fill_gradient(low ="#FFF60E", high ="#B70A7F", na.value ="grey90",limits =c(0, 100),labels =label_percent(scale =1)) +theme_bw() +theme(legend.title=element_blank())map_med
Figure 3: Percentage of population that dealt with anxiety and/or depression by taking prescribed medication in 2020 (by country)
Similarly, Figure 4 presents the proportion of population around the world that utilize social support to manage symptoms of anxiety and/or depression. The vast majority of countries report that 59% or more of their population engage in conversations with friends and family as a mean of coping – average percentage is 79.6%. Laos leads globally, with 95.4% of respondents relying on such support, whereas South Korea ranks lowest at 59%.
Figure 4: Percentage of population that dealt with anxiety and/or depression by talking to friends and family in 2020 (by country)
With both Figure 3 and 4 show the same countries with missing or underreported data, it can be infer that interpersonal connection (seen in Figure 4) is a more commonly used coping strategy across regions. As compared to pharmacological intervention, social support demonstrates broader application and utility across countries, with a global average of 79.6%, which is significantly higher than the 48.9% of individuals using prescribed medication. Hence, the value of incorporating interpersonal support into mental health care frameworks is hardly refutable.
Discussion
In 2022, Meda and colleagues showed that at the global level, GDP per capita significantly reduces suicide rates across regions. Their analysis, based on publicly available data from 1991 to 2017, highlighted that this effect was most pronounced in low-income countries, where suicide rates decreased by approximately 30% for every $1,000 increase in GDP per capita. The magnitude of this association diminished with increasing income levels: a 5% reduction was observed among lower-middle and middle-income countries, and a 2% reduction among high-income countries (Meda et al., 2022). However, our current project finds no significant differences in impact of income level on suicide rates as demonstrated by the researchers (Table 1). In fact, we were only able to determine a minimal negative relationship between GDP per capita and suicide rates across national income levels, with upper-middle income exhibiting the largest negative correlation. Although interesting, such a finding should be further investigated in future research to better understand why the effect may vary across income groups.
Regarding the positive correlation between GDP and suicide rates in Table 2, some research included in Stack et al. (2021) suggested a significant correlation between government spending and suicide rates. For example, Flavin and Radcliff (2009) discovered that American social welfare spending (e.g., transfer payments, medical benefits, and family aid) was associated with reduced suicide rates among states. In another study investigating thirty-one Organization for Economic Cooperation and Development (OECD) countries on their public social expenditure and suicides, the results demonstrated a link between the reduction in suicide rates and an increase in social welfare spending, even when controlling for GDP and income inequality. Consistent with these findings, our results also show that despite an increase in GDP per capita, some upper-middle and high income countries also exhibited a great increase in suicide rates between 2000 and 2021 (e.g., Uruguay, South Africa, Thailand, South Korea, Mexico, United States). Thus, regardless of whether or not a country exhibits an increase in GDP per capita over a period of time, death by suicide rates can also be influenced by the state’s social investment.
Furthermore, as shown in Skinner et al. (2023), both unemployment and underemployment were critical contributors to suicide mortality in Australia over 13 years. The study emphasized that transitions from employment to unemployment often lead to heightened psychological distress – a key precursor to suicide and intentional self-harm, as supported by findings from Erlangsen et al. (2020). Corroborating these results, another study identified the duration of unemployment as an additional determinant of suicide risk across various countries (Stack et al., 2021). Notably, Meda et al. (2022) demonstrated that this relationship is more pronounced among males, who face a higher risk of suicide during periods of unemployment compared to females. Therefore, it should be highlighted that economic growth alone does not uniformly translate to lower suicide rates across all contexts. While the inverse relationship between GDP per capita and suicide rates was strongly supported by prior research, other variables like social welfare spending and unemployment rate also critically shape mental health outcomes. Further research should thus take into account the factors mentioned above when analyzing the association between GDP per capita and suicide rates.
Our second analysis reveals notable global differences in coping mechanisms, with social support emerging as a more prevalent strategy compared to the use of prescribed medication (Figure 3; Figure 4). While limited empirical evidence was found to explain the widespread reliance on social support across countries, existing research has demonstrated its critical role in mitigating anxiety and depressive symptoms. For instance, Dour et al. (2014) found that greater perceived social support significantly mediated symptom improvement for U.S. adults with depression and anxiety disorders. In addition, previous studies have shown that higher levels of social support are inversely associated not only with relapse rates in depression but also with adverse physical health outcomes, including cardiovascular disease and cancer (Paykel, 1994; Berkman, 1995). One other study on the relationship between social support and mental health among Chinese frontline healthcare workers suggested that a heightened level of social integration and group relationships decreased one’s likelihood to exhibit symptoms of depression and anxiety (Zhan et al., 2022). Hence, it could be argued that social support is more universally prevalent because it serves not only as a protective factor against the onset and recurrence of mental health disorders, but also as a critical component in promoting long-term physiological well-being.
Moreover, we anticipate that the discrepancy in coping strategies is caused by the stricter regulations governing the use and implementation of pharmacological interventions compared to social support. One key challenge to providing biomedical intervention is resource allocation. According to Rathod et al. (2017), global spending on mental health in 2011 was under $2 per capita annually, which fell below 25 cents in low-income countries. For instance, in Pakistan, only 3.9% of the national GDP is allocated to the health sector, with mental health receiving just 0.4% of that expenditure. In India, the situation is similarly constrained, 4.6% of the federal budget is designated for health, while mental health only receives 0.06% of that general health budget (Rathod et al., 2017).
Psychopharmacology’s application could also be shaped by the country’s religious and political landscape. In Saudi Arabia, for example, psychoanalytic literature and theory were officially banned until the late 20th century. This restriction stemmed from religious authorities in earlier decades who deemed Freud’s contributions heretical (Algahtani et al., 2016). It was also suggested that countries with low Democracy Index exhibited lower government funding for mental health (Rajkumar, 2022). Nevertheless, even when mental health policies were put into place in countries with less authoritarian forms of government, the success ultimately depends on how well they are implemented. For instance, despite the U.S. policy progress, the ongoing housing affordability crisis has left many individuals with serious mental illness unable to access stable living conditions, which in turn severely limits the effectiveness of treatment and recovery (Martone, 2014).
Conclusion
In conclusion, our findings corroborated previous research in demonstrating the existing negative association between GDP per capita and suicide rate across nations (Table 1). Yet, this trend was not significant nor consistent globally and we were able to identify a subgroup that exhibited an opposite pattern (Table 1). Extensive research has been done on GDP per capita and its significant impact on social welfare; now it is the time to dive deeper into how nations utilization of capital resources and public opinion regarding psychological illnesses can impact the prospective of global mental health. As demonstrated earlier, GDP per capita only serves as a broader representation of national progress; other components such as national policy, social welfare expenditure, unemployment rate, cultural and religious believes altogether play a role in influencing the distribution and efficacy of mental health treatment on anxiety and depression, which ultimately affects suicide rates. Therefore, it would be valuable to incorporate these confounding variables in future analysis for a more comprehensive view on the relationship between national wealth and population well-being.
Additionally, we observed a notable disparity between the two coping strategies, with social support being more universally applied than pharmacological treatment in addressing mental health conditions. Several factors may account for this difference, including the inherent psychological benefits of interpersonal support and the regulatory requirements that can limit access to medication for anxiety and depression. Hence, we suggest that future research should examine the individual impact of each treatment on suicide rates. Building on this, further investigation could also explore the effects of combining prescribed medication with social support to determine whether integrative approaches lead to greater reductions in suicide rates compared to relying on a single form of intervention.
Limitations
One particular limitation of this project is the missing data from a considerable number of countries, which may have influenced the representativeness of our sample and potentially limited the generalizability of our findings.
Acknowledgements
I want to thank Professor Emily and Violet in providing new insights and ideas for this project – I have never enjoy coding like ever before. It was super fun to learn how to present my work visually.
References
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# Top 10 Countries with Largest Decrease in Suicide Rates -- Negative Correlational Tabletable_neg <- table %>%filter(gdp_positive ==TRUE& suicide_negative ==TRUE) %>%select(country, continent, suicide_rate_diff, gpd_diff, gdp_per_cap_in_2021, inc_level_2021) %>%arrange(suicide_rate_diff) %>%slice(1:10) %>%rename(Country = country,Continent = continent,"GDP in 2021 (in USD)"= gdp_per_cap_in_2021,"Income Level in 2021"= inc_level_2021,"GDP Increase between 2000 and 2021 (in USD)"= gpd_diff,"Suicide Rate Decrease between 2000 and 2021 (per 10,000 people)"= suicide_rate_diff)kable(table_neg, caption ="Table 3. Top 10 Countries Ranked by Differences in Suicide Rate between 2000 and 2021 -- Negative Correlation")
Table 3. Top 10 Countries Ranked by Differences in Suicide Rate between 2000 and 2021 – Negative Correlation
Country
Continent
Suicide Rate Decrease between 2000 and 2021 (per 10,000 people)
GDP Increase between 2000 and 2021 (in USD)
GDP in 2021 (in USD)
Income Level in 2021
Russia
Asia
-29.9
$10,750
$12,522
upper middle
Lithuania
Europe
-29.0
$20,634
$23,935
higher
Sri Lanka
Asia
-26.1
$3,153
$3,999
lower middle
Belarus
Europe
-23.7
$6,213
$7,490
upper middle
Kazakhstan
Asia
-19.4
$8,804
$9,984
upper middle
Latvia
Europe
-17.5
$16,985
$20,263
higher
Hungary
Europe
-15.2
$14,131
$18,755
higher
Ukraine
Europe
-13.9
$4,123
$4,776
upper middle
Rwanda
Africa
-13.3
$577
$829
lower
Estonia
Europe
-12.7
$23,883
$27,954
higher
Notes
Attempted Challenges:
Reshaped data with pivot_longer and pivot_wider to edit data and generate tables (Table 1 and 2).
Generated maps using the sf package (Figure 2 and 3).
Used gganimate to display changes overtime (Figure 1).
